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Silvia A. González, Joel D. Barnes, Patrick Abi Nader, Dolores Susana Andrade Tenesaca, Javier Brazo-Sayavera, Karla I. Galaviz, Marianella Herrera-Cuenca, Piyawat Katewongsa, Juan López-Taylor, Yang Liu, Bilyana Mileva, Angélica María Ochoa Avilés, Diego Augusto Santos Silva, Pairoj Saonuam and Mark S. Tremblay

letter grade on scatterplots. Associations within indicators and between indicators and sociodemographic indexes were then explored using Spearman’s rank correlation coefficients. Strong correlations were defined as coefficients equal to or higher than .7 or -.7. 37 Pairwise deletion was used to treat

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Ryan M. Hulteen, Lisa M. Barnett, Philip J. Morgan, Leah E. Robinson, Christian J. Barton, Brian H. Wrotniak and David R. Lubans

15 s posttest). The difference between the first and third measurement (i.e., heart rate recovery) was used within a regression equation to calculate VO 2 max ( Francis & Feinstein, 1991 ). Results in youth populations have found a correlation of r  = .81 between this measure and actual VO 2 max

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Jenny Meggs, Mark Chen and Danielle Mounfield

strength task conditions. This suggests that the role of prenatal testosterone in influencing performance is particularly evident when under pressure. However, this interpretation should be treated with caution as studies have reported equivocal findings (i.e., some studies have found negative correlations

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Gabrielle Ringenberg, Jill M. Maples and Rachel A. Tinius

by 1000 to obtain milliliters of oxygen consumed per kilogram lean body mass per minute which neutralizes the effect of body composition to better assess fitness. Statistical Analysis Based on the sample size ( N  = 18), the correlations between actual and predicted VO 2max , and the α value set at 0

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Yucui Diao, Cuixiang Dong, Lisa M. Barnett, Isaac Estevan, Jing Li and Liu Ji

who had tried each FMS item and those who had not tried it was tested with a chi-squared test ( p  < .05). Both an intra-class correlation and internal consistency were calculated to determine the reliability of the PMSC-TGMD-3 version. An intra-class correlation < 0.40 was rated as poor agreement, 0

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Keith R. Lohse, Jincheng Shen and Allan J. Kozlowski

evaluating change vary with data structure and trade-offs one may face in selecting from available methods. Garcia and Marder ( 2017 ) discuss three problems common to longitudinal data: (1) correlations within the data, (2) irregularly timed measurements, and (3) missing data. Correlations within data can

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Kathryn J. DeShaw, Laura Ellingson, Yang Bai, Jeni Lansing, Maria Perez and Greg Welk

FBC were compared to the SIP estimates while average daily steps were compared with the AP monitor. Pearson product correlations were computed to examine the overall association between the FBC and reference measures for estimated daily MVPA and steps. Mean percent errors (MPE) were calculated by

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Salih A. Salih, Nancye M. Peel, Di Enright and Wendy Marshall

. Depending on the distribution of the data, parametric or nonparametric paired sample tests were used to determine if the average PA and functional outcomes changed between admission and discharge. Correlation statistics, parametric and nonparametric as required, were used to determine the relationship

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Samuel G. Wittekind, Nicholas M. Edwards, Philip R. Khoury, Connie E. McCoy, Lawrence M. Dolan, Thomas R. Kimball and Elaine M. Urbina

calculated with the formula of Devereux et al, 17 and LVM index (LVMI = LVM/height 2.7 ) was calculated by de Simone et al’s method. 18 LVM measurement in our laboratory is excellent with correlation of .96 and .84 for intraobserver and interobserver variabilities, 19 and coefficient of variability for

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Lene Levy-Storms, Lin Chen and Anastasia Loukaitou-Sideris

, and results/outcomes in the reviewed articles. We did not undertake any formal assessment of risk of bias because all but one of the 48 studies were descriptive and/or correlational. In other words, the results from this synthesis do not highlight parameter estimates in outcomes, with the exception of